Overview

Dataset statistics

Number of variables33
Number of observations13265
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 MiB
Average record size in memory257.0 B

Variable types

CAT18
NUM14
BOOL1

Warnings

city has a high cardinality: 171 distinct values High cardinality
fireplace has a high cardinality: 77 distinct values High cardinality
parking has a high cardinality: 281 distinct values High cardinality
lot_f has a high cardinality: 360 distinct values High cardinality
architectural has a high cardinality: 387 distinct values High cardinality
sewer has a high cardinality: 102 distinct values High cardinality
water has a high cardinality: 74 distinct values High cardinality
app has a high cardinality: 146 distinct values High cardinality
heating has a high cardinality: 91 distinct values High cardinality
cooling has a high cardinality: 115 distinct values High cardinality
materials has a high cardinality: 160 distinct values High cardinality
roof has a high cardinality: 285 distinct values High cardinality
interior has a high cardinality: 446 distinct values High cardinality
compensation has a high cardinality: 365 distinct values High cardinality
tax_assessed is highly skewed (γ1 = 22.68487712) Skewed
covered is highly skewed (γ1 = 105.9219524) Skewed
garage is highly skewed (γ1 = 87.6396911) Skewed
total_spaces is highly skewed (γ1 = 63.59587501) Skewed
year is highly skewed (γ1 = 36.47242642) Skewed
df_index has unique values Unique
living has 169 (1.3%) zeros Zeros
lot_a has 839 (6.3%) zeros Zeros
tax_assessed has 570 (4.3%) zeros Zeros
stories has 1446 (10.9%) zeros Zeros

Reproduction

Analysis started2023-02-07 06:38:23.095485
Analysis finished2023-02-07 06:38:39.880703
Duration16.79 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct13265
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6668.134715
Minimum0
Maximum13338
Zeros1
Zeros (%)< 0.1%
Memory size103.6 KiB
2023-02-07T13:38:39.976517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile667.2
Q13325
median6674
Q310001
95-th percentile12671.8
Maximum13338
Range13338
Interquartile range (IQR)6676

Descriptive statistics

Standard deviation3852.224137
Coefficient of variation (CV)0.5777064054
Kurtosis-1.200666555
Mean6668.134715
Median Absolute Deviation (MAD)3338
Skewness-0.0004819924979
Sum88452807
Variance14839630.81
MonotocityStrictly increasing
2023-02-07T13:38:40.089608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01< 0.1%
 
54161< 0.1%
 
12901< 0.1%
 
33391< 0.1%
 
94861< 0.1%
 
115351< 0.1%
 
54001< 0.1%
 
74491< 0.1%
 
13061< 0.1%
 
33551< 0.1%
 
Other values (13255)1325599.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
133381< 0.1%
 
133371< 0.1%
 
133361< 0.1%
 
133351< 0.1%
 
133341< 0.1%
 

price
Real number (ℝ≥0)

Distinct2671
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1246741.354
Minimum0
Maximum165000000
Zeros2
Zeros (%)< 0.1%
Memory size103.6 KiB
2023-02-07T13:38:40.170307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile168000
Q1308888
median503000
Q3985000
95-th percentile3599600
Maximum165000000
Range165000000
Interquartile range (IQR)676112

Descriptive statistics

Standard deviation4107915.732
Coefficient of variation (CV)3.294922174
Kurtosis504.515843
Mean1246741.354
Median Absolute Deviation (MAD)246000
Skewness17.97946008
Sum1.653802406e+10
Variance1.687497166e+13
MonotocityNot monotonic
2023-02-07T13:38:40.243004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3500001190.9%
 
325000850.6%
 
299900840.6%
 
650000800.6%
 
399900790.6%
 
375000790.6%
 
450000770.6%
 
399000740.6%
 
300000730.6%
 
275000720.5%
 
Other values (2661)1244393.8%
 
ValueCountFrequency (%) 
02< 0.1%
 
199001< 0.1%
 
300002< 0.1%
 
329001< 0.1%
 
350001< 0.1%
 
ValueCountFrequency (%) 
1650000001< 0.1%
 
1500000001< 0.1%
 
1390000001< 0.1%
 
870000001< 0.1%
 
850000001< 0.1%
 

status
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
House for sale
10534 
Active
1827 
New construction
 
566
New
 
165
Foreclosure
 
67
Other values (4)
 
106
ValueCountFrequency (%) 
House for sale1053479.4%
 
Active182713.8%
 
New construction5664.3%
 
New1651.2%
 
Foreclosure670.5%
 
Price Change480.4%
 
Coming soon440.3%
 
Auction90.1%
 
Re-activated5< 0.1%
 
2023-02-07T13:38:40.313084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-02-07T13:38:40.354820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:40.419420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length14
Mean length12.8088202
Min length3

add_attr
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
True
10197 
False
3068 
ValueCountFrequency (%) 
True1019776.9%
 
False306823.1%
 
2023-02-07T13:38:40.458019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

city
Categorical

HIGH CARDINALITY

Distinct171
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Dallas
 
749
Indianapolis
 
743
Philadelphia
 
742
Chicago
 
736
Jacksonville
 
725
Other values (166)
9570 
ValueCountFrequency (%) 
Dallas7495.6%
 
Indianapolis7435.6%
 
Philadelphia7425.6%
 
Chicago7365.5%
 
Jacksonville7255.5%
 
San Antonio7195.4%
 
Charlotte7145.4%
 
Houston7045.3%
 
Columbus6785.1%
 
Fort Worth6765.1%
 
Other values (161)607945.8%
 
2023-02-07T13:38:40.524782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique43 ?
Unique (%)0.3%
2023-02-07T13:38:40.955311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length9
Mean length9.071617037
Min length4

state
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
TX
3369 
CA
2140 
AZ
772 
OH
755 
IN
750 
Other values (9)
5479 
ValueCountFrequency (%) 
TX336925.4%
 
CA214016.1%
 
AZ7725.8%
 
OH7555.7%
 
IN7505.7%
 
PA7425.6%
 
IL7375.6%
 
FL7325.5%
 
NC7265.5%
 
TN6765.1%
 
Other values (4)186614.1%
 
2023-02-07T13:38:41.023992image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-02-07T13:38:41.079937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

lat
Real number (ℝ≥0)

Distinct13141
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.7714584
Minimum0
Maximum47.733967
Zeros1
Zeros (%)< 0.1%
Memory size103.6 KiB
2023-02-07T13:38:41.146606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.6019648
Q132.742405
median35.158978
Q339.875687
95-th percentile41.9049724
Maximum47.733967
Range47.733967
Interquartile range (IQR)7.133282

Descriptive statistics

Standard deviation4.406290668
Coefficient of variation (CV)0.1231789495
Kurtosis-0.3195735213
Mean35.7714584
Median Absolute Deviation (MAD)4.587467
Skewness0.292664207
Sum474508.3957
Variance19.41539745
MonotocityNot monotonic
2023-02-07T13:38:41.221092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
40.0150833< 0.1%
 
34.0402682< 0.1%
 
35.233632< 0.1%
 
41.9030082< 0.1%
 
39.671352< 0.1%
 
39.7667242< 0.1%
 
32.7154052< 0.1%
 
36.1032332< 0.1%
 
36.1961062< 0.1%
 
38.9651642< 0.1%
 
Other values (13131)1324499.8%
 
ValueCountFrequency (%) 
01< 0.1%
 
29.1386991< 0.1%
 
29.14871< 0.1%
 
29.1666891< 0.1%
 
29.1986271< 0.1%
 
ValueCountFrequency (%) 
47.7339671< 0.1%
 
47.7331621< 0.1%
 
47.7328071< 0.1%
 
47.7318231< 0.1%
 
47.730671< 0.1%
 

long
Real number (ℝ)

Distinct13102
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-95.38698208
Minimum-122.508575
Maximum0
Zeros1
Zeros (%)< 0.1%
Memory size103.6 KiB
2023-02-07T13:38:41.294036image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-122.508575
5-th percentile-121.9514552
Q1-105.034775
median-95.51751
Q3-82.950386
95-th percentile-74.977019
Maximum0
Range122.508575
Interquartile range (IQR)22.084389

Descriptive statistics

Standard deviation15.04957552
Coefficient of variation (CV)-0.1577738931
Kurtosis-0.936499418
Mean-95.38698208
Median Absolute Deviation (MAD)12.55348
Skewness-0.3737761375
Sum-1265308.317
Variance226.4897233
MonotocityNot monotonic
2023-02-07T13:38:41.368161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-95.413592< 0.1%
 
-122.3684842< 0.1%
 
-95.502362< 0.1%
 
-96.761722< 0.1%
 
-98.658862< 0.1%
 
-86.165662< 0.1%
 
-111.997462< 0.1%
 
-104.9103242< 0.1%
 
-96.65962< 0.1%
 
-75.039452< 0.1%
 
Other values (13092)1324599.8%
 
ValueCountFrequency (%) 
-122.5085751< 0.1%
 
-122.508411< 0.1%
 
-122.507721< 0.1%
 
-122.507591< 0.1%
 
-122.506771< 0.1%
 
ValueCountFrequency (%) 
01< 0.1%
 
-73.70411< 0.1%
 
-73.7045441< 0.1%
 
-73.705731< 0.1%
 
-73.709511< 0.1%
 

bath
Real number (ℝ≥0)

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.888880513
Minimum0
Maximum27
Zeros16
Zeros (%)0.1%
Memory size103.6 KiB
2023-02-07T13:38:41.430522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile6
Maximum27
Range27
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.648422178
Coefficient of variation (CV)0.5706093316
Kurtosis19.0611743
Mean2.888880513
Median Absolute Deviation (MAD)1
Skewness2.998985167
Sum38321
Variance2.717295678
MonotocityNot monotonic
2023-02-07T13:38:41.489670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
2502337.9%
 
3386029.1%
 
4161212.2%
 
1140010.6%
 
55754.3%
 
63032.3%
 
71941.5%
 
81100.8%
 
9630.5%
 
10350.3%
 
Other values (14)900.7%
 
ValueCountFrequency (%) 
0160.1%
 
1140010.6%
 
2502337.9%
 
3386029.1%
 
4161212.2%
 
ValueCountFrequency (%) 
271< 0.1%
 
251< 0.1%
 
241< 0.1%
 
201< 0.1%
 
191< 0.1%
 

bed
Real number (ℝ≥0)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.66038447
Minimum0
Maximum17
Zeros18
Zeros (%)0.1%
Memory size103.6 KiB
2023-02-07T13:38:41.543255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile6
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.11779903
Coefficient of variation (CV)0.3053774922
Kurtosis7.162295297
Mean3.66038447
Median Absolute Deviation (MAD)1
Skewness1.456711574
Sum48555
Variance1.249474671
MonotocityNot monotonic
2023-02-07T13:38:41.594300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
3547841.3%
 
4436132.9%
 
5156111.8%
 
210718.1%
 
64763.6%
 
71321.0%
 
1710.5%
 
8540.4%
 
9190.1%
 
0180.1%
 
Other values (5)240.2%
 
ValueCountFrequency (%) 
0180.1%
 
1710.5%
 
210718.1%
 
3547841.3%
 
4436132.9%
 
ValueCountFrequency (%) 
171< 0.1%
 
143< 0.1%
 
126< 0.1%
 
115< 0.1%
 
1090.1%
 

living
Real number (ℝ≥0)

ZEROS

Distinct3963
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2417.703958
Minimum0
Maximum56500
Zeros169
Zeros (%)1.3%
Memory size103.6 KiB
2023-02-07T13:38:41.660096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile923
Q11406
median1940
Q32772
95-th percentile5367.4
Maximum56500
Range56500
Interquartile range (IQR)1366

Descriptive statistics

Standard deviation2004.2348
Coefficient of variation (CV)0.8289827186
Kurtosis86.38769582
Mean2417.703958
Median Absolute Deviation (MAD)620
Skewness6.289224095
Sum32070843
Variance4016957.132
MonotocityNot monotonic
2023-02-07T13:38:41.730345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01691.3%
 
1200610.5%
 
1800420.3%
 
2000400.3%
 
1600380.3%
 
3000380.3%
 
1440370.3%
 
1500370.3%
 
2200350.3%
 
2100330.2%
 
Other values (3953)1273596.0%
 
ValueCountFrequency (%) 
01691.3%
 
12< 0.1%
 
3001< 0.1%
 
3921< 0.1%
 
4001< 0.1%
 
ValueCountFrequency (%) 
565001< 0.1%
 
380001< 0.1%
 
371321< 0.1%
 
360001< 0.1%
 
314501< 0.1%
 

lot_a
Real number (ℝ≥0)

ZEROS

Distinct4553
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4167.410015
Minimum0
Maximum10863.864
Zeros839
Zeros (%)6.3%
Memory size103.6 KiB
2023-02-07T13:38:41.806159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.2
median4356
Q36882.48
95-th percentile9583.2
Maximum10863.864
Range10863.864
Interquartile range (IQR)6881.28

Descriptive statistics

Standard deviation3314.051039
Coefficient of variation (CV)0.7952303775
Kurtosis-1.251204581
Mean4167.410015
Median Absolute Deviation (MAD)3005.64
Skewness0.1004388909
Sum55280693.84
Variance10982934.29
MonotocityNot monotonic
2023-02-07T13:38:41.882268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
08396.3%
 
7405.21431.1%
 
65341391.0%
 
6098.41351.0%
 
4791.61311.0%
 
5227.21240.9%
 
7840.81180.9%
 
43561150.9%
 
8276.41090.8%
 
6969.61080.8%
 
Other values (4543)1130485.2%
 
ValueCountFrequency (%) 
08396.3%
 
0.131< 0.1%
 
0.1461< 0.1%
 
0.1721< 0.1%
 
0.211< 0.1%
 
ValueCountFrequency (%) 
10863.8641< 0.1%
 
10846.441< 0.1%
 
108461< 0.1%
 
10837.7281< 0.1%
 
10833.3721< 0.1%
 

tax_assessed
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct10381
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean651513.3831
Minimum0
Maximum120990610
Zeros570
Zeros (%)4.3%
Memory size103.6 KiB
2023-02-07T13:38:41.957523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15020
Q1160439
median288000
Q3567602
95-th percentile1887436.8
Maximum120990610
Range120990610
Interquartile range (IQR)407163

Descriptive statistics

Standard deviation2036394.823
Coefficient of variation (CV)3.125637748
Kurtosis1017.783391
Mean651513.3831
Median Absolute Deviation (MAD)168300
Skewness22.68487712
Sum8642325027
Variance4.146903877e+12
MonotocityNot monotonic
2023-02-07T13:38:42.035166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05704.3%
 
5000190.1%
 
4500080.1%
 
19500080.1%
 
36000080.1%
 
20500070.1%
 
23000070.1%
 
600006< 0.1%
 
1827006< 0.1%
 
2600006< 0.1%
 
Other values (10371)1262095.1%
 
ValueCountFrequency (%) 
05704.3%
 
1001< 0.1%
 
2001< 0.1%
 
4951< 0.1%
 
7001< 0.1%
 
ValueCountFrequency (%) 
1209906101< 0.1%
 
498303711< 0.1%
 
454662001< 0.1%
 
348465681< 0.1%
 
336377131< 0.1%
 

fireplace
Categorical

HIGH CARDINALITY

Distinct77
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
none
5968 
1.0
4233 
2.0
729 
living room
 
409
not applicable
 
370
Other values (72)
1556 
ValueCountFrequency (%) 
none596845.0%
 
1.0423331.9%
 
2.07295.5%
 
living room4093.1%
 
not applicable3702.8%
 
family room3172.4%
 
1 fireplace2441.8%
 
3.02261.7%
 
gas log970.7%
 
4.0820.6%
 
Other values (67)5904.4%
 
2023-02-07T13:38:42.119798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique29 ?
Unique (%)0.2%
2023-02-07T13:38:42.189040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length4
Mean length4.588239729
Min length2

parking
Categorical

HIGH CARDINALITY

Distinct281
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Garage
1395 
Attache
 
680
none
 
663
Attached Garag
 
552
Drivewa
 
534
Other values (276)
9441 
ValueCountFrequency (%) 
Garage139510.5%
 
Attache6805.1%
 
none6635.0%
 
Attached Garag5524.2%
 
Drivewa5344.0%
 
Attached4973.7%
 
Garage - Attached4873.7%
 
On Street4003.0%
 
Driveway3933.0%
 
2-Car Single Door3762.8%
 
Other values (271)728854.9%
 
2023-02-07T13:38:42.263534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique49 ?
Unique (%)0.4%
2023-02-07T13:38:42.342244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length9
Mean length10.50931021
Min length3

covered
Real number (ℝ≥0)

SKEWED

Distinct353
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.005438647
Minimum1
Maximum424
Zeros0
Zeros (%)0.0%
Memory size103.6 KiB
2023-02-07T13:38:42.412233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.775
median2
Q32
95-th percentile3
Maximum424
Range423
Interquartile range (IQR)0.225

Descriptive statistics

Standard deviation3.771700635
Coefficient of variation (CV)1.880735988
Kurtosis11820.86061
Mean2.005438647
Median Absolute Deviation (MAD)0.05
Skewness105.9219524
Sum26602.14365
Variance14.22572568
MonotocityNot monotonic
2023-02-07T13:38:42.483507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2582243.9%
 
1165812.5%
 
38466.4%
 
42311.7%
 
1.721110.8%
 
1.75960.7%
 
1.65930.7%
 
1.82930.7%
 
1.975910.7%
 
1.995890.7%
 
Other values (343)413531.2%
 
ValueCountFrequency (%) 
1165812.5%
 
1.0451< 0.1%
 
1.0853< 0.1%
 
1.1351< 0.1%
 
1.144< 0.1%
 
ValueCountFrequency (%) 
4241< 0.1%
 
601< 0.1%
 
201< 0.1%
 
161< 0.1%
 
141< 0.1%
 

garage
Real number (ℝ≥0)

SKEWED

Distinct352
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.989149726
Minimum1
Maximum424
Zeros0
Zeros (%)0.0%
Memory size103.6 KiB
2023-02-07T13:38:42.556377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.74
median2
Q32
95-th percentile3
Maximum424
Range423
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation4.175753276
Coefficient of variation (CV)2.099265441
Kurtosis8350.620268
Mean1.989149726
Median Absolute Deviation (MAD)0.055
Skewness87.6396911
Sum26386.07111
Variance17.43691543
MonotocityNot monotonic
2023-02-07T13:38:42.626667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2578443.6%
 
1161012.1%
 
38246.2%
 
41811.4%
 
1.6851110.8%
 
1.6251060.8%
 
1.9451030.8%
 
1.985990.7%
 
1.78930.7%
 
1.54930.7%
 
Other values (342)426132.1%
 
ValueCountFrequency (%) 
1161012.1%
 
1.021< 0.1%
 
1.031< 0.1%
 
1.074< 0.1%
 
1.092< 0.1%
 
ValueCountFrequency (%) 
4241< 0.1%
 
2121< 0.1%
 
601< 0.1%
 
131< 0.1%
 
121< 0.1%
 

total_spaces
Real number (ℝ≥0)

SKEWED

Distinct327
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.488030839
Minimum1
Maximum424
Zeros0
Zeros (%)0.0%
Memory size103.6 KiB
2023-02-07T13:38:42.700210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32.395
95-th percentile5
Maximum424
Range423
Interquartile range (IQR)0.395

Descriptive statistics

Standard deviation4.800363733
Coefficient of variation (CV)1.92938273
Kurtosis4993.342948
Mean2.488030839
Median Absolute Deviation (MAD)0.105
Skewness63.59587501
Sum33003.72908
Variance23.04349197
MonotocityNot monotonic
2023-02-07T13:38:42.770433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2559542.2%
 
1168212.7%
 
39877.4%
 
48926.7%
 
62642.0%
 
52001.5%
 
2.1751271.0%
 
8770.6%
 
2.01740.6%
 
2.52710.5%
 
Other values (317)329624.8%
 
ValueCountFrequency (%) 
1168212.7%
 
1.3551< 0.1%
 
1.3953< 0.1%
 
1.471< 0.1%
 
1.4856< 0.1%
 
ValueCountFrequency (%) 
4241< 0.1%
 
2121< 0.1%
 
2031< 0.1%
 
801< 0.1%
 
601< 0.1%
 

lot_f
Categorical

HIGH CARDINALITY

Distinct360
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
none
5360 
Corner Lo
692 
Back Yar
 
461
Cul-De-Sa
 
453
Level
 
441
Other values (355)
5858 
ValueCountFrequency (%) 
none536040.4%
 
Corner Lo6925.2%
 
Back Yar4613.5%
 
Cul-De-Sa4533.4%
 
Level4413.3%
 
Curb4373.3%
 
Subdivide3852.9%
 
Leve3032.3%
 
Few Tree2231.7%
 
North/South Exposur2201.7%
 
Other values (350)429032.3%
 
2023-02-07T13:38:42.850171image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique114 ?
Unique (%)0.9%
2023-02-07T13:38:42.934088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length38
Median length5
Mean length7.620203543
Min length3

subtype
Categorical

Distinct27
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Single Family Residence
10639 
Residentia
1079 
Detached
 
501
none
 
382
Residential
 
361
Other values (22)
 
303
ValueCountFrequency (%) 
Single Family Residence1063980.2%
 
Residentia10798.1%
 
Detached5013.8%
 
none3822.9%
 
Residential3612.7%
 
Single Family - Detached1200.9%
 
Ranch460.3%
 
All Other Attached400.3%
 
Single Family - Semi-Attached230.2%
 
Residential-Detache140.1%
 
Other values (17)600.5%
 
2023-02-07T13:38:43.008052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6 ?
Unique (%)< 0.1%
2023-02-07T13:38:43.076535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length29
Median length23
Mean length20.40904636
Min length4

architectural
Categorical

HIGH CARDINALITY

Distinct387
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
none
4967 
Traditional
2158 
Ranch
927 
Contemporary
510 
2.0
 
372
Other values (382)
4331 
ValueCountFrequency (%) 
none496737.4%
 
Traditional215816.3%
 
Ranch9277.0%
 
Contemporary5103.8%
 
2.03722.8%
 
Colonial3592.7%
 
Bungalow3352.5%
 
TraditonalAmerican2832.1%
 
Other2081.6%
 
Straight Thru1981.5%
 
Other values (377)294822.2%
 
2023-02-07T13:38:43.155943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique210 ?
Unique (%)1.6%
2023-02-07T13:38:43.244011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length67
Median length5
Mean length8.188239729
Min length3

year
Real number (ℝ≥0)

SKEWED

Distinct213
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1966.036383
Minimum0
Maximum9999
Zeros15
Zeros (%)0.1%
Memory size103.6 KiB
2023-02-07T13:38:43.328227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1905
Q11941
median1967
Q31999
95-th percentile2022
Maximum9999
Range9999
Interquartile range (IQR)58

Descriptive statistics

Standard deviation124.0166378
Coefficient of variation (CV)0.06307952329
Kurtosis2723.908651
Mean1966.036383
Median Absolute Deviation (MAD)28
Skewness36.47242642
Sum26079472.62
Variance15380.12646
MonotocityNot monotonic
2023-02-07T13:38:43.407987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20226645.0%
 
19253332.5%
 
19502702.0%
 
19202541.9%
 
19552331.8%
 
19002291.7%
 
19402061.6%
 
20062051.5%
 
20052011.5%
 
19602001.5%
 
Other values (203)1047078.9%
 
ValueCountFrequency (%) 
0150.1%
 
17301< 0.1%
 
17501< 0.1%
 
18071< 0.1%
 
18291< 0.1%
 
ValueCountFrequency (%) 
99992< 0.1%
 
2023680.5%
 
20226645.0%
 
2021.9570.1%
 
2021790.6%
 

sewer
Categorical

HIGH CARDINALITY

Distinct102
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Public Sewer
7196 
none
2584 
City Sewer
1409 
Sewer Connecte
 
444
SAW
 
201
Other values (97)
1431 
ValueCountFrequency (%) 
Public Sewer719654.2%
 
none258419.5%
 
City Sewer140910.6%
 
Sewer Connecte4443.3%
 
SAW2011.5%
 
Sewer System1821.4%
 
Septic Tank1240.9%
 
Septic System1220.9%
 
Sewer Connected1050.8%
 
SAWS1010.8%
 
Other values (92)7976.0%
 
2023-02-07T13:38:43.495311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique31 ?
Unique (%)0.2%
2023-02-07T13:38:43.583636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length12
Mean length10.11443649
Min length1

water
Categorical

HIGH CARDINALITY

Distinct74
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Public
6897 
none
2157 
City Water
1079 
Publi
 
549
Meter on Property
 
384
Other values (69)
2199 
ValueCountFrequency (%) 
Public689752.0%
 
none215716.3%
 
City Water10798.1%
 
Publi5494.1%
 
Meter on Property3842.9%
 
Lake Michigan3812.9%
 
City Wate3212.4%
 
SAW2622.0%
 
Water System2011.5%
 
Water District1741.3%
 
Other values (64)8606.5%
 
2023-02-07T13:38:43.667311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique25 ?
Unique (%)0.2%
2023-02-07T13:38:43.744478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length6
Mean length7.014700339
Min length3

app
Categorical

HIGH CARDINALITY

Distinct146
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
dishwasher
5037 
none
1736 
electric water heater
553 
gas water heater
544 
built-in microwave
543 
Other values (141)
4852 
ValueCountFrequency (%) 
dishwasher503738.0%
 
none173613.1%
 
electric water heater5534.2%
 
gas water heater5444.1%
 
built-in microwave5434.1%
 
range5314.0%
 
cooktop3022.3%
 
electric cooktop2952.2%
 
gas cooktop2682.0%
 
dryer2581.9%
 
Other values (136)319824.1%
 
2023-02-07T13:38:43.823258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique33 ?
Unique (%)0.2%
2023-02-07T13:38:43.903503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length38
Median length10
Mean length10.90629476
Min length3

heating
Categorical

HIGH CARDINALITY

Distinct91
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
central
4711 
forced air
2917 
natural gas
1998 
electric
887 
none
694 
Other values (86)
2058 
ValueCountFrequency (%) 
central471135.5%
 
forced air291722.0%
 
natural gas199815.1%
 
electric8876.7%
 
none6945.2%
 
hot water2662.0%
 
fireplace(s1661.3%
 
radiator1421.1%
 
central forced air1281.0%
 
other1240.9%
 
Other values (81)12329.3%
 
2023-02-07T13:38:43.985685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique23 ?
Unique (%)0.2%
2023-02-07T13:38:44.062074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length8
Mean length8.879080286
Min length2

cooling
Categorical

HIGH CARDINALITY

Distinct115
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
Central Air
4833 
Ceiling Fan(s
1519 
Central Ai
1201 
None
827 
none
720 
Other values (110)
4165 
ValueCountFrequency (%) 
Central Air483336.4%
 
Ceiling Fan(s151911.5%
 
Central Ai12019.1%
 
None8276.2%
 
none7205.4%
 
Electri5424.1%
 
Central A/5063.8%
 
Electric4153.1%
 
Central Forced Air3072.3%
 
Refrigeration2151.6%
 
Other values (105)218016.4%
 
2023-02-07T13:38:44.137001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique21 ?
Unique (%)0.2%
2023-02-07T13:38:44.209182image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length11
Mean length10.3928383
Min length2

stories
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.499246136
Minimum0
Maximum7
Zeros1446
Zeros (%)10.9%
Memory size103.6 KiB
2023-02-07T13:38:44.262490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8558670917
Coefficient of variation (CV)0.570864964
Kurtosis0.215134403
Mean1.499246136
Median Absolute Deviation (MAD)1
Skewness0.2602398322
Sum19887.5
Variance0.7325084787
MonotocityNot monotonic
2023-02-07T13:38:44.312250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
1532640.2%
 
2500537.7%
 
0144610.9%
 
311338.5%
 
3.51491.1%
 
2.5930.7%
 
4790.6%
 
1.5270.2%
 
53< 0.1%
 
73< 0.1%
 
ValueCountFrequency (%) 
0144610.9%
 
1532640.2%
 
1.5270.2%
 
2500537.7%
 
2.5930.7%
 
ValueCountFrequency (%) 
73< 0.1%
 
61< 0.1%
 
53< 0.1%
 
4790.6%
 
3.51491.1%
 

materials
Categorical

HIGH CARDINALITY

Distinct160
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
brick
4169 
none
2082 
stucco
771 
vinyl sideing
638 
frame
528 
Other values (155)
5077 
ValueCountFrequency (%) 
brick416931.4%
 
none208215.7%
 
stucco7715.8%
 
vinyl sideing6384.8%
 
frame5284.0%
 
masonry4713.6%
 
wood sideing4093.1%
 
frame - woo3872.9%
 
stone3642.7%
 
block3382.5%
 
Other values (150)310823.4%
 
2023-02-07T13:38:44.385685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique43 ?
Unique (%)0.3%
2023-02-07T13:38:44.454791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length5
Mean length7.264304561
Min length3

roof
Categorical

HIGH CARDINALITY

Distinct285
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
none
4443 
Composition
4131 
Shingle
1342 
Tile
560 
Asphalt
445 
Other values (280)
2344 
ValueCountFrequency (%) 
none444333.5%
 
Composition413131.1%
 
Shingle134210.1%
 
Tile5604.2%
 
Asphalt4453.4%
 
Comp Shingle2852.1%
 
Metal1891.4%
 
Other1791.3%
 
Composition,Shingle1501.1%
 
Flat1471.1%
 
Other values (275)139410.5%
 
2023-02-07T13:38:44.525648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique149 ?
Unique (%)1.1%
2023-02-07T13:38:44.600008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length57
Median length7
Mean length8.069204674
Min length4

foundation
Categorical

Distinct50
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
none
5838 
slab
3666 
concrete perimeter
700 
crawl space
 
487
pillar/post/pier
 
480
Other values (45)
2094 
ValueCountFrequency (%) 
none583844.0%
 
slab366627.6%
 
concrete perimeter7005.3%
 
crawl space4873.7%
 
pillar/post/pier4803.6%
 
poured concrete4563.4%
 
blogck3332.5%
 
other2281.7%
 
brick/mortar1611.2%
 
stone1591.2%
 
Other values (40)7575.7%
 
2023-02-07T13:38:44.680875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique11 ?
Unique (%)0.1%
2023-02-07T13:38:44.751360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length4
Mean length6.405955522
Min length4

interior
Categorical

HIGH CARDINALITY

Distinct446
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
none
3939 
Ceiling Fan(s
 
585
Cable TV Availabl
 
569
Built-in Feature
 
539
One Living Are
 
360
Other values (441)
7273 
ValueCountFrequency (%) 
none393929.7%
 
Ceiling Fan(s5854.4%
 
Cable TV Availabl5694.3%
 
Built-in Feature5394.1%
 
One Living Are3602.7%
 
Walk-In Closet(s3262.5%
 
Breakfast Ba2521.9%
 
Two Living Are2391.8%
 
High Ceiling2391.8%
 
Walk-In Closet(s)2361.8%
 
Other values (436)598145.1%
 
2023-02-07T13:38:44.828409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique186 ?
Unique (%)1.4%
2023-02-07T13:38:44.909002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length38
Median length13
Mean length11.32672446
Min length2

compensation
Categorical

HIGH CARDINALITY

Distinct365
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size103.6 KiB
none
3994 
3%
2581 
2.5%
2074 
% Of Gross2.5
648 
2.50%
501 
Other values (360)
3467 
ValueCountFrequency (%) 
none399430.1%
 
3%258119.5%
 
2.5%207415.6%
 
% Of Gross2.56484.9%
 
2.50%5013.8%
 
2.8%4463.4%
 
2.500%3842.9%
 
3.00%3092.3%
 
2%2501.9%
 
% Of Gross31781.3%
 
Other values (355)190014.3%
 
2023-02-07T13:38:44.991581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique225 ?
Unique (%)1.7%
2023-02-07T13:38:45.076217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length66
Median length4
Mean length5.370825481
Min length2

Interactions

2023-02-07T13:38:25.670959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:25.742679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:25.809523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:25.872684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:25.938816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.003717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.075348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.141769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.206907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.275058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.339372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.402045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.467097image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.533050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.603304image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.670997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.737243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.802001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.868056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:26.940048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.009263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.079441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.147852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.219484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.285165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.355165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.423781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.490988image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.566629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.640411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.720595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.782638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.846760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.912826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:27.979641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.044687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.114776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.182978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.242754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.302381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.363265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.425937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.490151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.556534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.622013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.684781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.757103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.823274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.896008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:28.960520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:29.028890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:29.093259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:29.154313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:29.215774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:29.276604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:29.340885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:29.404787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:29.468438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:29.531647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:29.590460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:30.517142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:30.579358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:30.643309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:30.704098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:30.763112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:30.826215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:30.883566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:30.941955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:30.999361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.060366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.126843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.194755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.262551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.326237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.391721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.456217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.522645image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.588465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.652255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.724975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.793275image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.858191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.928191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:31.996602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.062178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.125432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.188810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.248280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.310257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.369061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.430584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.491089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.551865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.614113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.673379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.731460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.790759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.851433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.913044image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:32.982854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.044808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.103191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.164615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.223593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.287050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.348822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.409483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.472235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.530146image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.589245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.647965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.709020image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.775769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.842932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.909980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:33.974077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:34.038767image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:34.101953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:34.170324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:34.235242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:34.300454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:34.367577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:34.430857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:34.493954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:34.558221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:34.626100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:34.692918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:34.754865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:34.815030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.038067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.098153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.155262image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.215213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.272531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.329798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.390666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.445391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.500591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.557628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.616159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.675834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.736993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.797009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.853033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.913392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:35.969458image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.030281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.088534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.145105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.205919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.262112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.317648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.374462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.432702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.492588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.553080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.614007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.671071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.729699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.788734image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.851641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.910629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:36.970136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.030108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.087508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.143042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.200021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.259319image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.320574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.383025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.444955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.504628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.565199image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.625164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.686407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.747195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.805859image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.867460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.926704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:37.984285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.043183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.103536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.164256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.230062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.295149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.357387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.421889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.484230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.552827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.621959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.687796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.754950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.819148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.880949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:38.950160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:39.015271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2023-02-07T13:38:45.148882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-02-07T13:38:45.261979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-02-07T13:38:45.376778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-02-07T13:38:45.501774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2023-02-07T13:38:45.616191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2023-02-07T13:38:39.231058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-07T13:38:39.707716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Sample

First rows

df_indexpricestatusadd_attrcitystatelatlongbathbedlivinglot_atax_assessedfireplaceparkingcoveredgaragetotal_spaceslot_fsubtypearchitecturalyearsewerwaterappheatingcoolingstoriesmaterialsrooffoundationinteriorcompensation
00274000House for saleTrueSouth Ozone ParkNY40.675730-73.8223501315562400.00noneGarage - Detached1.7501.5951.945nonenonenone1930.0nonenonenonenonenone0.0nonenonenonenonenone
11270000House for saleTrueJamaicaNY40.670036-73.7804502419203998.0678000nonenone1.6601.5352.160nonenonenone1950.0nonenonemicrowavenonenone0.0nonenonenonenonenone
22899000House for saleTrueStaten IslandNY40.524227-74.2157903325326903.0637000noneDetache1.9352.0002.000noneSingle Family - DetachedColonial1899.0Public Sewernonedishwasherhot waterUnits2.0nonenonenonenonenone
331390000House for saleTrueFlushingNY40.721615-73.8207554419152697.0894000nonenone1.6951.6553.915nonenonenone1945.0nonenonedryernonenone0.0nonenonenonenonenone
451380000House for saleTrueBrooklynNY40.604470-73.9439604318002000.00noneShared Drivewa1.7001.0002.160Near Public TransitSingle Family Residence2 Story1930.0Public SewerPublicdishwashernatural gasWall Unit(s)2.0bricknonenoneFormal Dining Roonone
56599000House for saleTrueBrooklynNY40.639286-73.9412702313441950.0534000nonenone1.8001.6852.520nonenonenone1925.0nonenonedryernonenone0.0nonenonenonenonenone
671280000House for saleTrueBrooklynNY40.622246-74.0178302314222613.0882000noneGarage - Detached1.6051.5351.000nonenonenone1920.0nonenonedishwasherradianCentral0.0noneShake / Shinglenonenonenone
79899000House for saleTrueBrooklynNY40.578552-74.0051963428003000.0419000nonenone1.7201.6702.000nonenonenone1945.0nonenonedishwashernonenone0.0nonenonenonenonenone
810565000House for saleTrueJamaicaNY40.682640-73.7881802318961951.0406000noneGarage - Attached1.8001.6852.520nonenonenone1925.0nonenonedishwashernonenone0.0noneShake / Shinglenonenonenone
91199999House for saleTrueStaten IslandNY40.627700-74.179020128402100.00noneCommunity Driv2.1402.1503.000Back YarSingle Family ResidenceRanch1998.0nonenonenonenatural gasnone1.0aluminum sideinFlat,Metalothernonenone

Last rows

df_indexpricestatusadd_attrcitystatelatlongbathbedlivinglot_atax_assessedfireplaceparkingcoveredgaragetotal_spaceslot_fsubtypearchitecturalyearsewerwaterappheatingcoolingstoriesmaterialsrooffoundationinteriorcompensation
1325513329385000House for saleTrueNashvilleTN36.158485-86.8282401311000.252700001.0Driveway1.8501.8502.000Unknown Soil TypeSingle Family ResidenceCape Cod1925.0Public SewerPublicgas water heaterforced airCentral A/3.0aluminum sideinnoneothernone% Of Gross2
1325613330599000House for saleTrueNashvilleTN36.104990-86.7457603325328712.00280200noneDriveway1.9651.9552.395Unknown Soil TypeSingle Family ResidenceColonial1976.0Public SewerPublicelectric water heaterforced airCentral A/2.0bricknonebrick/mortarnone% Of Gross2.5
1325713331299000House for saleTrueNashvilleTN36.219044-86.756760139600.41153300noneNone1.9151.7402.135Unknown Soil TypeSingle Family ResidenceContemporary1947.0Public SewerPublicoven/range - gashot waterCentral A/3.0brickWood,Otherslabnone% Of Gross2.9
13258133321149900House for saleTrueNashvilleTN36.193188-86.777830442012871.200noneOff Street1.9201.7802.560Urban Land-Sassafras-ChillumSingle Family ResidenceColonial1936.0Public SewerPublicgas water heaterforced airNone3.0bricknonebrick/mortarDining Are% Of Gross2
13259133333675000House for saleTrueNashvilleTN36.123570-86.7955806557040.29961800noneConcrete Drivewa1.9051.8852.000Unknown Soil TypeSingle Family ResidenceTraditional1953.0Public SewerPublicbuilt-in microwavecentralCentral A/3.0brickFlatslabDining Are% Of Gross2.5
1326013334750000House for saleTrueNashvilleTN36.160694-86.8478303322084356.00563900noneDrivewa1.5701.6902.030Unknown Soil TypeSingle Family ResidenceColonial1951.0Public SewerPublicgas water heaterforced airCentral A/3.0bricknoneslabnone% Of Gross2.5
1326113335575000House for saleTrueNashvilleTN36.204834-86.7386602436589147.600noneOn Stree1.8051.7802.000Unknown Soil TypeSingle Family ResidenceTraditional1940.0Public SewerPublicdishwasherforced airCentral A/3.0bricknoneslabKitchen - Gourme% Of Gross3
1326213336609000House for saleTrueNashvilleTN36.196106-86.735115331950871.200noneOn Street1.8001.6852.520Urban Land-Sassafras-ChillumSingle Family ResidenceColonial1925.0Public SewerPublicgas water heaterhot waterDuctless/Mini-Spli3.0bricknonebrick/mortarCeiling Fan(s)% Of Gross2.5
1326313337460000Coming soonTrueNashvilleTN36.026558-86.7177102322247840.803196002.0Drivewa1.8951.8252.090LandscapinSingle Family ResidenceColonial1937.0Public SewerPublicbuilt-in microwaveforced airCentral A/3.0combinationUnknownotherCeiling Fan(s% Of Gross2.5
13264133384300000House for saleTrueNashvilleTN36.134396-86.8227105545160.361415000noneOn Street1.8851.8552.460UrbaSingle Family ResidenceTraditional1925.0Public SewerPublicgas water heaterhot waterCentral A/3.0brickFlat,Rubberothernone% Of Gross2.5